/awesome-graph-embedding

(We will not update in the future) A collection of important graph embedding, classification and representation learning papers with implementations.

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Graph Embedding

A collection of graph embedding, deep learning, graph kernel and factorization papers with reference implementations.

Table of Contents
  1. Factorization
  2. Spectral and Statistical Fingerprints
  3. Deep Learning
  4. Graph Kernels
  5. Papers in Chinese

Paper References with the implementation(s)

Factorization

Spectral and Statistical Fingerprints

  • NetLSD (KDD 2018)

    • Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and Muller, Emmanuel
    • [Paper]
    • [Python Reference]

  • A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)

  • Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)

  • Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)

  • Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)

  • NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)

    • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos
    • [Paper]
    • [Python]

Deep Learning

  • Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)

  • Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

  • Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)

  • MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)

  • Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)

  • Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)

  • Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)

  • Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)

  • An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)

  • Graph Classification Using Structural Attention (KDD 2018)

    • Lee, John Boaz and Rossi, Ryan and Kong, Xiangnan
    • [Paper]

  • SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)

  • Deep Learning with Topological Signatures (NIPS 2017)

  • Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)

  • Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

  • Graph Classification with 2D Convolutional Neural Networks (2017)

    • Tixier, Antoine J-P and Nikolentzos, Giannis and Meladianos, Polykarpos and Vazirgiannis, Michalis
    • [Paper]
    • [Python Reference]

  • Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)

  • Kernel Graph Convolutional Neural Networks (2017)

    • Nikolentzos, Giannis and Meladianos, Polykarpos and Tixier, Antoine Jean-Pierre and Skianis, Konstantinos and Vazirgiannis, Michalis
    • [Paper]
    • [Python Reference]

  • Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)

  • Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)

    • Duvenaud, David K and Maclaurin, Dougal and Iparraguirre, Jorge and Bombarell, Rafael and Hirzel, Timothy and Aspuru-Guzik, Alan and Adams, Ryan P
    • [Paper]
    • [Python Reference]

Graph Kernels

  • Message Passing Graph Kernels (2018)

  • Matching Node Embeddings for Graph Similarity (AAAI 2017)

  • Global Weisfeiler-Lehman Graph Kernels (2017)

  • On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)

  • Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)

  • The Multiscale Laplacian Graph Kernel (NIPS 2016)

  • Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)

  • Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)

  • Halting Random Walk Kernels (NIPS 2015)

  • Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)

    • Feragen, Aasa and Kasenburg, Niklas and Petersen, Jens and de Bruijne, Marleen and Borgwardt, Karsten
    • [Paper]

  • Subgraph Matching Kernels for Attributed Graphs (ICML 2012)

  • Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)

  • Weisfeiler-Lehman Graph Kernels (JMLR 2011)

  • Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

  • A Linear-time Graph Kernel (ICDM 2009)

  • Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

  • Fast Computation of Graph Kernels (NIPS 2006)

  • Shortest-Path Kernels on Graphs (ICDM 2005)

  • Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)

  • Extensions of Marginalized Graph Kernels (ICML 2004)

    • Mahe, Pierre and Ueda, Nobuhisa and Akutsu, Tatsuya and Perret, Jean-Luc and Vert, Jean-Philippe
    • [Paper]
    • [Python Reference]

  • Marginalized Kernels Between Labeled Graphs (ICML 2003)

Papers in Chinese

  • 网络表示学习综述

    • 涂存超, 杨成, 刘知远, 孙茂松, 2017.

  • 网络表示学习

    • 陈维政, 张岩, 李晓明, 2015.

  • 图计算在阿里巴巴的应用与挑战

    • 钱正平, 周靖人, 2018.

网络表征学习前沿与实践:

  • 属性网络表征学习的研究与发展

    • 胡侠, 黄啸, 杨红霞, 2018.

  • 异质网络表征学习的研究进展

    • 石川, 孙怡舟, 2018.

  • 网络表征学习在微信平台的应用

    • 孙仕杰, 易玲玲, 2018.

  • 全图表征学习的研究进展

    • 唐建, 2018.

  • 网络表征学习中的基本问题初探

    • 王啸, 崔鹏, 朱文武, 2018.